Association of umbilical cord insulin-like growth factor 1 levels with severe retinopathy in extremely preterm infants

Histological Chorioamnionitis and Funisitis as New Risk Factors for Retinopathy of Prematurity: A Meta-analysis

OBJECTIVE

 The role of placental inflammation in neonatal morbidities is underestimated due to lack of placental examination. This meta-analysis aims to assess the association between histological chorioamnionitis (HCA) with and without funisitis (FUN) and risk of retinopathy of prematurity (ROP).

STUDY DESIGN

 Forty-five studies reporting (unadjusted) data on HCA without FUN and HCA with FUN in neonates with ROP were included. Primary outcomes were any stage ROP and severe ROP. Potential confounders explored were gestational age (GA) at birth, birthweight, maternal steroid use, necrotizing enterocolitis, sepsis (suspected/proven) and mechanical ventilation duration.

RESULTS

 Neonates with HCA had increased risk for any stage ROP (odds ratio [OR] 1.8; 95% confidence interval [CI] 1.3-2.4) and severe ROP (OR 1.5; 95% CI 1.2-1.8) compared with neonates without HCA. The rates of any stage ROP (OR 1.8; 95% CI 1.4-2.2) and severe ROP (OR 1.4; 95% CI 1.1-1.6) were higher in neonates with FUN compared with neonates without FUN. Multivariate meta-regression analysis suggests that lower GA increases the effect size between FUN and severe ROP.

CONCLUSION

 This meta-analysis confirms that presence of HCA and FUN are risk factors for any stage ROP and severe ROP. Structured histological placental examination of HCA and FUN may be a tool to further refine the ROP risk profile.

KEY POINTS

· This systematic review confirms that HCA is a risk factor for ROP.. · This meta-analysis reveals that FUN results in an even higher risk for developing ROP.. · Placental examination of HCA/FUN may be a tool to further refine the ROP risk profile..

Identification of novel biomarkers for retinopathy of prematurity in preterm infants by use of innovative technologies and artificial intelligence

Retinopathy of prematurity (ROP) is a leading cause of preventable vision loss in preterm infants. While appropriate screening is crucial for early identification and treatment of ROP, current screening guidelines remain limited by inter-examiner variability in screening modalities, absence of local protocol for ROP screening in some settings, a paucity of resources and an increased survival of younger and smaller infants. This review summarizes the advancements and challenges of current innovative technologies, artificial intelligence (AI), and predictive biomarkers for the diagnosis and management of ROP. We provide a contemporary overview of AI-based models for detection of ROP, its severity, progression, and response to treatment. To address the transition from experimental settings to real-world clinical practice, challenges to the clinical implementation of AI for ROP are reviewed and potential solutions are proposed. The use of optical coherence tomography (OCT) and OCT angiography (OCTA) technology is also explored, providing evaluation of subclinical ROP characteristics that are often imperceptible on fundus examination. Furthermore, we explore several potential biomarkers to reduce the need for invasive procedures, to enhance diagnostic accuracy and treatment efficacy. Finally, we emphasize the need of a symbiotic integration of biologic and imaging biomarkers and AI in ROP screening, where the robustness of biomarkers in early disease detection is complemented by the predictive precision of AI algorithms.